Channel impulse response estimation

ABSTRACT

A human-machine interface system adapted to track movement of an object in air is disclosed. In one aspect, the human-machine interface includes an acoustic transmitter, an acoustic receiver, and logic configured to apply a calculated inverse matrix of an impulse signal transmitted by the acoustic transmitter to a signal received by the acoustic receiver, wherein movement of the object is used to control the machine.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. application Ser. No.12/848,930, entitled “CHANNEL IMPULSE RESPONSE ESTIMATION” and filedAug. 2, 2010, which is a continuation of U.S. application Ser. No.11/766,651, filed Jun. 21, 2007, which is a continuation of PCTInternational Application Number PCT/GB2005/004968, filed on Dec. 21,2005, which claims priority under 35 U.S.C. §119(e) to U.S. provisionalpatent application 60/637,437, filed on Dec. 21, 2004. Each of theabove-described applications is hereby incorporated by reference in itsentirety.

BACKGROUND

1. Field of the Invention

This invention relates to the estimation of the response of a channel toan impulse to allow mapping of that environment or one or more objectstherein; particularly but not exclusively using acoustic impulses. Itrelates in particular to measuring real time changes in the environment.

2. Description of Related Technology

There are many diverse applications in which it is desirable to be ableto use acoustic signals to gain information about an environment.Examples include people tracing, human-computer interfaces, seabedmapping, medical ultrasound imaging, acoustic scanners, security devicesand robot vision. Using acoustic signals rather than light can haveseveral advantages—for example it is insensitive to light conditions,works well with frequencies undetectable by humans and can offerimproved or simplified depth perception.

The basic principle of acoustic mapping is that a short burst or impulseis emitted into the environment and the reflection from an object ofinterest in the environment is measured to determine something about it,usually its size, shape or position. This is known in the art asestimating the impulse response of the channel or simply estimating thechannel. The main obstacle encountered in channel estimation foracoustic monitoring is that of unwanted echoes which arise in virtuallyall realistic applications. If the channel and observer are both staticthis is not a serious problem. However if there is any element of timevariation in the system, it is necessary to transmit impulses repeatedlyin order to measure the changes. A problem occurs when there isinterference between a transmitted pulse and echoes from previouspulses. This problem is demonstrated with reference to FIGS. 1 a to 1 d.

FIG. 1 a shows an impulse is driven through an output device (e.g. aloudspeaker or transducer), and the resulting signal received through aninput device (e.g. a microphone, a sensor). The impulse response isshown as a time series, where the horizontal axis is the time axis andthe vertical axis shows the response (e.g. the pressure) at the giventime. The estimated impulse response can give important informationabout the channel and hence the surroundings; the position of an object(“the reflector”); deformities and shapes. Estimation of the impulseresponse is the basis of a vast range of signal processing applications,ranging from wireless communication through medical imaging and seismicexplorations.

However there are many situations in which the surroundings couldchange, or the channel vary in another way. It is desirable to be ableto monitor this change. Clearly to give the best temporal resolution onewould need to measure the impulse response as frequently as possible. Itmight appear that an obvious way to do this would be to transmitimpulses more often, as shown in FIG. 1( b); but this gives rise to aproblem. When an impulse is sent shortly after a previous one, echoesfrom the first impulse response ‘colour’ the next estimate, as can beseen from the wavy patterns in FIG. 1( b) between the main peaks. Eachestimate of the channel is therefore distorted to some extent by thelast estimate. Although this is a kind of noise in that it masks thewanted signal, it is not like random environmental noise which could beovercome by increasing the signal strength or by averaging oversuccessive estimates. Instead it is “structured” noise as it is derivedfrom the signal itself and cannot be corrected for without knowing thechannel perfectly which is of course the original objective.

The severity of this problem increases when the output impulses areemitted even more frequently, as shown in FIG. 1( c). Looking at asingle “impulse response slice” (see FIG. 1( d)) from the waveform ofFIG. 1( c) and comparing it to the corresponding response from FIG. 1(a) when just one impulse was transmitted, it will be seen that eventhough in this example the physical channel has not changed, the attemptat frequent estimation of the channel has failed; the estimate measuredis dramatically different and so would give a highly inaccurate result.

The example above highlights a dilemma in channel estimation. In orderto estimate a channel accurately (and hence, find the position of e.g. astrong reflector) it is necessary to use a “send-receive-wait” method.In other words it is necessary to: send a transmit signal; receive thereturning signal; and wait unit all echoes have died out beforetransmitting again. This avoids the estimation being ruined by thestructured noise arising from lingering echoes from earlier impulses.However though this severely limits the temporal resolution which isachievable and therefore the temporal tracking capabilities of manyschemes (such as seabed mapping equipment or finger positioningapparatus/computer-human interfaces).

What is needed is a way of continuously, or continually and frequently,obtaining accurate estimates of the impulse response of a channel; oralternatively, monitoring or tracking the impulse response.

A previous proposal for continuously estimating a channel has been madeby Klahold & al in “Continuous Sonar Sensing for Mobile Mini-robots”(Proceedings of the IEEE International Conference on Robotics andInformation (ICRA0”), pp. 323-328, 2002). However, these methods arebased on so-called cross-correlation techniques. As a result they havesignificant shortcomings. In particular, whilst such methods can be usedwith sliding windows of the transmitted and received signals tocontinually update the estimate of the channel's impulse response, itcan be shown that as well as being heavily dependent on using a longenough window, the optimum performance of these techniques relies onperfect piecewise orthogonality of input signals. In other words eachsample of the transmitted signal must be orthogonal with every othersample. This is very hard to achieve—especially when it is consideredthat in a sliding window the signal is simply incrementally shiftedalong so that each element of the input sample code must be orthogonalwith the corresponding element of the same code a window-length away.Schemes relying on cross-correlation are dominated by this need to findsequences which fulfill the requirement for orthogonality as adequatelyas possible. Some examples of attempts at such codes are given by chaoscoding, Gold Sequences and Golan codes. However none of these hasproduced satisfactory results.

SUMMARY OF CERTAIN INVENTIVE ASPECTS

When viewed from a first aspect the present invention provides a methodof estimating the impulse response of a channel comprising transmittingan impulse signal to said channel, detecting a received signal from thechannels and calculating an estimate of the impulse response of thechannel by applying a calculated inverse matrix of the impulse signal tothe received signal.

The invention extends to an apparatus for estimating a channel responsecomprising transmission means, receiving means and processing meanswherein said processing means is arranged to apply a calculated inversematrix of an impulse signal transmitted by the transmission means to asignal received by the receiving means.

The invention also extends to: a computer software product forestimating the impulse response of a channel comprising means forapplying a calculated inverse matrix of a matrix representing atransmitted signal to a plurality of samples of a received responsesignal; and to a carrier bearing such software.

Thus in accordance with the invention a calculated inverse matrix of thetransmitted impulse signal is applied to the received signal. It can beshown that this gives a reliable estimate of the channel withoutrequiring successively orthogonal codes and without having to resort tothe crude send-receive-wait technique. Furthermore since the inversematrix is based on the transmitted signal, even though it may becomputationally demanding, it need not be calculated in real-time sincethe transmitted signal, and hence the calculated inverse matrix, can bepredetermined.

BRIEF DESCRIPTION OF THE DRAWINGS

FIGS. 1 a-1 c show signal traces from signal impulses received atdifferent frequencies.

FIGS. 1 d and 1 e show magnified signal traces of single impulses fromthe traces shown in FIGS. 1 a and 1 c, respectively.

FIG. 2 shows a mathematical model of the channel.

FIG. 3 shows a mathematical model of the channel from avector-space/inner-product point of view.

FIG. 4 shows a mathematical model of the channel with a series ofsnapshot vectors.

FIG. 5 shows a matrix with successive time slices.

FIG. 6 shows an example of a sequence of impulse response estimates.

FIG. 7 shows an example of a picture from an embodiment.

DETAILED DESCRIPTION OF CERTAIN EMBODIMENTS

As used herein the term “calculated inverse matrix” is to be understoodto mean a generalised inverse matrix which is calculated, using anappropriate matrix inversion technique, from a matrix representing orcharacteristic of the output signal. It will be clear to those skilledin the art that this is very different to an inverse of the signalitself.

There are a large number of possible appropriate methods for calculatingsuch a matrix which result in correspondingly different calculatedinverse matrices. Suitable possible methods of calculating inversematrices include: ridge regression; partial least squares (PLS),conjugate gradient (CG) based methods; information theoretical methods,such as those based on the maximum entropy (MAXENT) principle;regression based on other norms and criteria such as the L^(P) norm;maximum likelihood regression; and regression based on variable subsetselection. In addition, the use of the regression model to emulateclassification or clustering-like properties, for the purpose ofsegregating different qualities of the signal parameters into differentclasses, such as discriminant analysis, optimal scoring, (generalized)canonical correlation analysis, local regression methods, functionalanalysis regression and kernel methods are envisaged within the scope ofthe invention. Further details regarding these methods are given in“Elements of statistical learning” by T. Hastie, R. Tibshirani and J.Friedman, the Springer Series in Statistics, 1001, Springer Verlag.

In preferred embodiments however the calculated inverse matrix is thepseudo-inverse matrix, or Moore-Penrose inverse matrix. As is known inthe art, this is the unique minimum-norm least squares solution to theassociated linear equation.

The invention can be used with a single impulse signal burst. Preferablyhowever the impulse signal is persistent i.e. the impulse signal couldbe transmitted continuously, at regular intervals, spasmodically or inanother other continuing mode. This allows of course the monitoring ofvariation of the channel's impulse response with time without therestriction of the send-receive-wait paradigm and so is where theinvention delivers greatest benefit.

The invention may be used to estimate the impulse response of anychannel to any sort of impulse. In particular the impulse could compriseelectromagnetic waves from any part of the spectrum, e.g.: microwaves;visible, ultraviolet or infrared light; radio waves; X rays etc. Atpresent the Applicants see particular benefit in the estimation ofresponse to acoustic impulses such as can be used in the applicationsmentioned in the introduction. Thus in some preferred embodiments thetransmitted impulse comprises an acoustic impulse. Within this set ofembodiments, the frequency band used will depend on the application. Forexample one important application envisaged is the tracking of an objectin air over distances of the order of less than a metre (e.g. for acomputer-human interface). For this and similar applications ultrasonicsignals are suitable. Thus in one set of preferred embodiments theinvention the impulse is ultrasonic; preferably the centre of thebandwidth of the signal is greater than 20 kHz, more preferably greaterthan 30 kHz and most preferably between 30 and 50 kHz.

Another application envisaged is in seismic imaging. Here the impulse islikely to have a broad bandwidth that could extend down to the order ofHertz.

The ability of the calculated inverse matrix to give a goodapproximation to the channel being measured will now be demonstratedusing the example of the preferred pseudo-inverse matrix.

It may first be assumed that the transmission of a signal over a channelfollows the following model;

y(t)=h(t)*x(t)+n(t)  Eq (1)

where:x(t) is the signal transmittedy(t) is the received signalh(t) is the channel's impulse responsen(t) is an environmental noise term* denotes a convolution operator

The transmitted signal is represented as a time series—that is to say aseries of discrete signal values at regular time intervals. The receivedsignal is also represented as a time series since it will be a sampledsignal. The impulse response h(t) is what is being sought to bemeasured. It is assumed that the channel h(t) is constant or changingvery slowly relative to the changes in x(t) and y(t), at least withinany actual time-window used. That is not to say that time-varyingchannels cannot be measured, indeed that is the object of the invention,just that the channel variation should be slow compared with the signalvariation.

The channel can be represented as a Q-tap finite impulse response (FIR)filter. As is well known in the art this is a construction in which thechannel h(t) is seen as a series of weights to be applied to theprevious Q time samples of the input signal. Eq (1) can therefore berewritten as follows:

$\begin{matrix}{{y(t)} = {{{{h(t)}*{x(t)}} + {n(t)}} = {{\sum\limits_{i = 0}^{Q - 1}\; {{x\left( {t - i} \right)}{h(i)}}} + {n(t)}}}} & {{Eq}\mspace{14mu} (2)}\end{matrix}$

An interpretation of the model is as follows. At a given time t, the Qlast time-samples of x(t−Q+1), . . . x(t), are linearly combinedaccording to the weights given in h(0) . . . h(Q−1) to give the currentinput signal y(t). The model is illustrated in FIG. 2. To simplify thecalculations, there is, for the time being, assumed to be noenvironmental noise, i.e. n(t)=0 for all t. The issue of noise isaddressed further below.

The process could also be seen from a vector-space/inner-product pointof view. A “snapshot” of the Q−1 latest realizations of the process x(t)is put into a vector x(t), and its inner product with the vector hcontaining filter coefficients h(0), . . . h(Q−1) taken as the resulty(t), as illustrated in FIG. 3.

What is required is an estimate of the vector h. This vector cannot beestimated unambiguously from a single observation y(t) of the inputsignal and one vector x(t) alone. To get sufficient information about hfor unambiguous information, what is required is a series of“snapshot-vectors” x(t), x(t−1), . . . from the x(t)-process, and onesnapshot-vector y(t)=[y(t−N+1), . . . y(t)] of the y(t)-process. Then,the response-snapshot-vector y(t) can be written via an inner product ofh with each of the “snapshot-vectors” x(t), x(t−1), . . . as illustratedin FIG. 4.

A final version of the matrix set-up can be seen by noting that theelements of X(t) can be written as:

X(t)_(ij) =x(t−i−j) 0≦j≦Q−1, 0≦i≦N−1  Eq (3)

It can now be seen that the first column of X(t) contains the time-sliceof x(t) from x(t−Q+1) to x(t−Q−N+2), going backwards in time from top tobottom. The second column of X(t) contains the time-slice of x(t) fromx(t−Q+2) to x(t−Q−N+3), etc, and the final column contains thetime-slice of x(t) going from x(t−N+1) going backwards in time. Insummary, the columns of the matrix X(t) contains successive time slicesof x(t), as illustrated in FIG. 5.

It can now be seen that the output signal vector y(t) is a linearcombination of the last Q time-snapshot vectors of x(t). This is avectorized version of the original statement of the model.

As long as the matrix X(t) is sufficiently well conditioned, the impulseresponse h can be estimated in a number of different ways, by picking anestimate ĥ in accordance with a suitable criterion, such as

$\begin{matrix}{{\hat{h} = {\min \; \arg_{h}{{{{X(t)}h} - y}}_{2}^{2}}}{\hat{h} = {{\min \; \arg_{h}{{{{X(t)}h} - y}}_{2}^{2}} + {\alpha {h}_{2}^{2}}}}} & {{Eqs}\mspace{14mu} (4)}\end{matrix}$

Where α is a regularization parameter used to shrink the solutioncandidate in the presence of noise and/or co-linearity in the predictingvariables.

The solutions to equations 4 and 5 above are the well known ordinaryleast-squares (OLS) estimates and ridge or least mean squares (LMS)estimates,

ĥ _(LS) =X(t)⁺ y

ĥ _(LMS)=(X(t)^(T) X(t)+αI)⁺ X(t)^(T) y  Eqs (5)

Where the “+” operator denotes the Moore-Penrose inverse matrix. This isknown in the art and simplistically it is the best approximation, usinga least squares approach, to the solution for a set of linear equations.Where a plurality of solutions exist, the minimum L2 norm one is chosen.

If the noise level is known, the α parameter could be chosen to minimizethe statistical mean square error. This technique is known as the LMSmethod in the art of signal processing. In such cases, however, it isnormally assumed that the matrix X(t) contains all of the samplesreceived from a single driving signal or compressed pulse. In accordancewith the present invention however it is proposed to use an X(t) matrixwhich contains only a subset of the driving signals. This allows theestimates of h to be, updated over time, without having to wait for asignal to die out before transmitting first. This effectively gives asliding sample window.

The above-mentioned sliding-window formulation of an LMS method isessentially a continuous LMS method as opposed to one using just asingle pulse or pulses spaced to avoid overlap. This is not to beconfused with time-continuous LMS methods which simply refers to havinga time-continuous (i.e. analogue) representation of the signals ratherthan having discrete samples.

It can be seen from the foregoing analysis that applying a calculatedinverse matrix, such as the pseudo-inverse matrix, of the transmittedsignal to the received signal gives a good estimate of the acousticchannel without requiring mutual orthogonality throughout thetransmitted signal. In fact the important factor in determiningperformance is the extent to which respective portions of thetransmission signal are linearly independent of one another. This ismuch less stringent that mutual orthogonality. The importance of linearindependence arises from equations Eqs (5) in which having the portionsof the signal as linearly independent as possible minimises ambiguitiesin the channel estimation. Such ambiguities would reduce theconditioning quality of the matrix X(t), leading to more possiblesolutions to the preceding equations, Eqs (4). Furthermore, the variancein the estimates of h would increase, since a co-linearity in thecolumns of X(t) implies that X(t) has some small singular values, whichin turn, upon pseudo-inversion for example, has the effect of blowing upadditive noise components.

As is shown later on, the inversion technique of the invention suffersdramatically less from the problem of structured noise which has beendescribed. The reason for this is that because this structured noiseforms part of the received signal, it will have the calculated inversematrix applied to it. This can be shown to have the effect ofrandomising or whitening the structured noise thus making it easy toaverage away.

It is a significant consideration that the calculation of inversematrices such as pseudo-inverses matrices is a computationally intensivetask. The required inverse matrix could be computed at the same time orimmediately prior to applying it to the received signal. In simpleapplications it might be possible to do this in real time as the impulsesignal is transmitted. However this will limit the resolution (bothtemporal and spatial) which can be achieved. To counter this the channelestimation could be carried out as ex post facto analysis. However thiswould make it unsuitable for many applications.

Preferably though the inverse matrix is calculated prior to transmittingthe impulse signal. The Applicant has appreciated that the ability to dothis it is an important advantage of preferred methods in accordancewith the invention. This allows real-time implementation without thedrastic limits on resolution or expensive processing power requirements.The calculated inverse matrices; e.g. X(t)⁺ can be computed in advancefrom the predetermined signal x(t) and stored in memory, then used as alook-up table for pre-multiplication with snapshot vectors of samples iny(t). There is no need for real-time inversion. The significance of thisbenefit can be better appreciated by comparing it to recent prior artteaching in US2005/0018539A1 in which a model is constructed such thatit is necessary to invert a matrix built up from time-samples of theresponse signal y(t), in which case post measurement inversion isinevitable. The emphasis of the teaching therein is therefore directedto ways of dividing the processing necessary between a number ofcomputers over distributed network. By contrast an embodiment of thepresent invention has been successfully implemented on a standarddesktop personal computer as will be described later.

The received signal is normally the result of a reflection from thesurfaces of objects in the environment and the effect of the medium inwhich the signal is travelling (e.g. air far acoustic signals). Equallyhowever the received signal could have passed through part of anenvironment without reflection. This could be the case for example witha simple system for positioning a transmitter (or an object to which itis attached) in space.

The transmitted signal, and therefore the received response, could bebroadband signals within the constraints of the hardware employed. In atleast some preferred embodiments however they are deliberately limitedin bandwidth. This is advantageous as it reduces the amount ofcomputation required to calculate the channel estimate since limitingthe bandwidth of an otherwise broadband signal corresponds tointroducing redundancy into the signal.

To take an example, the bandwidth might be limited to a third of itsoriginal range. Subsequent time-samples in the transmitted impulse x(t)and the received signal, y(t) will exhibit a degree of redundancy if thevector elements extracted from the full vector are chosen so as not tocause aliasing effects, since only approximately a third of the numberof multiplications in the inner products that would otherwise be neededis sufficient to obtain close correlation with the full inner productscontained in the matrix operations.

Furthermore, when the transmitted and received signals x(t) and y(t) areband-limited, so will the channel estimate ĥ be. It is then sufficientto estimate a reduced number of taps in ĥ (again if the filter taps in ĥare chosen so as to avoid aliasing) to be able to predict the other tapswith sufficient prediction quality for a number of applications. Thisagain reduces the amount of computation required and so broadens thepossible applications and/or increases the speed at which the analysiscan be carried out, which in turn allows a greater temporal resolutionto be achieved. Preferably the samples are chosen randomly or accordingto a pseudo-random sequence—since a regularly spaced selection would beequivalent simply to reducing the sampling rate.

In one set of preferred embodiments the channel estimation is used tomonitor all of the features of an environment. For example it could beused to measure the surface of a sea-bed which is of great value for oilexploration. Here the ‘channel’ is the sea bed and intervening water.The advantage which can be achieved in accordance with the invention isthat the surface may be scanned far more rapidly than with prior arttechniques whilst maintaining adequate resolution. This is a result ofthe ability given by the invention to provide a continuous update of thechannel estimate without having to wait for sent signals to die out andwithout having to construct transmission codes which are as highlymutually orthogonal as possible. Other examples of such embodiments arein medical imaging, security scanning, robotic vision etc.

When viewed from a further aspect the invention provides a method ofmapping an environment comprising transmitting an impulse signal intothe environment, detecting a return signal from the environment andapplying a calculated inverse matrix of the impulse signal to the returnsignal.

Also provided is a mapping apparatus for mapping an environmentcomprising means for transmitting an impulse signal into theenvironment, means for detecting a return signal from the environmentand applying a calculated inverse matrix of the impulse signal to thereturn signal.

The invention extends to a computer software product for mapping anenvironment comprising means for applying a calculated inverse matrix ofan impulse signal transmitted into the environment to a return signalfrom the environment; and means for calculating therefrom arepresentation of the environment.

Preferably said mapping method comprises constructing a position diagrambased on the estimated channel response. This is a representation inwhich successive impulse response estimates are presented adjacent oneanother. Features of such diagrams such as the edges or centre of areflector can then be identified using standard image processingtechniques. For example by moving the transmitter at a constant speedover a terrain such as sea-bed, the profile of the sea-bed will bescanned.

In another set of preferred embodiments the channel estimation is usedto track a moving object. Here the ‘channel’ is the object andsurrounding space. The ability to update the channel estimatecontinuously is critical here. Such applications were simply notpossible with prior art send-receive-wait techniques unless the objectwas moving very slowly or a very low spatial resolution was sufficient.As will be seen in the example below, an embodiment of the invention hasbeen shown to be able to track rapid movements of a fingertip tosub-millimetre accuracy using a standard desktop PC, an ordinary PCloudspeaker and two ordinary microphones. This has many possibleapplications in human-machine interfaces that obviate the need forphysical contact. One particular such application which is envisaged bythe applicant is in an interface for medical equipment, particularly inhospitals in which avoiding the need for physical contact reduces therisk of cross-infection via the equipment.

When viewed from a yet further aspect the invention provides a method oftracking an object comprising transmitting an impulse signal, recordinga reflection of said impulse signal from said object and applying acalculated inverse matrix of the impulse signal to said reflectedsignal.

Preferably said method also comprises constructing a position diagrambased on the estimated channel response and interpreting the saidposition diagrams, e.g. using suitable image processing techniques inorder to calculate the position of the object being tracked.

Also provided is a tracking apparatus comprising transmission means,receiving means and processing means wherein said processing means isarranged to apply a calculated inverse matrix of an impulse signaltransmitted by the transmission means to a signal received by thereceiving means.

The invention extends to a computer software product for tracking anobject comprising means for applying a calculated inverse matrix of animpulse signal transmitted towards the object into the environment to areturn signal reflected from the object; and means for calculatingtherefrom the location of the object.

In general each pairing of a transmitter and receiver allows tracking inone dimension. Hence for two dimensional tracking two receivers or,preferably, two transmitters are required. A common transmitter orreceiver respectively can be used in these two cases. However this isnot essential and separate ones could be used instead. The twotransmitters could be driven by independent signals. Alternatively theycould be driven by the same signal except for a time delay being appliedto one with respect to the other. This allows the signals to bedistinguished from one another. The same effect can be achieved byphysically offsetting one of the transmitters from the tacking region.

In some envisaged embodiments two transmitters, e.g. loudspeakers, aredriven by respective signals from the same frequency band, the signalsbeing separated at the receiver. This can be seen below:

The signal received at the receiver (e.g. microphone) is a combinationof the signals transmitted (and convolved):

y(t)=h ₁(t)*x ₁(t)+h ₂(t)*x ₂(t)  Eq (6a)

or in matrix/vector notation:

$\begin{matrix}{{{y(t)} = {{{X_{1}(t)}{h_{1}(t)}} + {{X_{2}(t)}{h_{2}(t)}}}}{{y(t)} = {{\begin{bmatrix}{X_{1}(t)} & {X_{2}(t)}\end{bmatrix}\begin{pmatrix}{h_{1}(t)} \\{h_{2}(t)}\end{pmatrix}} = {{X(t)}{h(t)}}}}{where}{{X(t)} = {\begin{bmatrix}{X_{1}(t)} & {X_{2}(t)}\end{bmatrix}\mspace{14mu} {and}}}{{h(t)} = {\begin{bmatrix}{{h_{1}(t)}^{\bigwedge}T} & {{h_{2}(t)}^{\bigwedge}T}\end{bmatrix}^{\bigwedge}{T.}}}} & {{Eqs}\mspace{11mu} \left( {6b} \right)}\end{matrix}$

Using a method in accordance with the invention, the vector h(t),containing both of the impulse responses h₁(t) and h₂(t), can beestimated, ensuring that the dimensions of the matrix X(t) are chosensuch that the result is stable with respect to errors and perturbations.

Of course for three-dimensional tracking an extra transmitter orreceiver can be added. The method of the invention is ideally suited tobeing used with multiple transmitters and receivers as can beappreciated from the analysis. For example these multiple elements mightbe used to in order to perform array processing and array imagegeneration and analysis. Indeed the method of the invention is powerfulin the range of applications and sound and image generation techniqueswith which it can be used. This stems from the ability to carry out thecomputationally intensive calculations which result from inverting thesignal matrix in advance of actual operation.

To locate an object on two or three dimensions the different signalsmust be suitably combined. Preferably ellipse intersection is used tocombine the impulse response estimates corresponding to the differentsignals to locate the object being tracked. In other words byinterpreting a peak in the impulse response as due to a strongreflector, the reflector is taken to lie on an ellipsoid having thereceiver/transmitter as focal points, and a radius given by thepropagation time from the transmitter to the reflector and back. Ifmultiple transmitters/receivers are used, the reflector lies on theintersection of the ellipsoids corresponding to thereceiver-transmitter-propagation time triploids, and can be identifiedas the common intersection point of these ellipsoids.

The mapping and tracking uses of the invention set out above have animportant feature in common: namely that they are closed in the sensethat the receiver receives and analyses only the response of the channelto the impulse the transmitter generates. Indeed this is true in generalin accordance with the invention; no information is communicated betweenthe transmitter and receiver. The invention is predicated on thereceiver knowing exactly what will be transmitted, albeit that this istransformed into the calculated inverse matrix. Logically no informationcan be passed since the ‘information’ would be needed to calculate theinverse matrix.

Preferably the sampling rate employed for the transmitted signal andreceived response is the same. However this is not essential—they couldbe different. Depending on the application and the resolution requiredsampling rates of anything from a few Hertz up to Gigahertz could beused.

The Applicant has appreciated that the continuous LMS method set forthin accordance with the invention is advantageous as it robust againstlingering echoes and noise. This will be demonstrated below. Includingboth lingering echoes and noise in Eq (1) gives:

$\begin{matrix}{{y(t)} = {{{{h(t)}*{x(t)}} + {n(t)}} = {{\sum\limits_{i = 0}^{Q - 1}\; {{x\left( {t - i} \right)}{h(i)}}} + {n(t)} + {s(t)}}}} & {{Eq}\mspace{14mu} (7)}\end{matrix}$

Where n(t) is the channel noise, and s(t) is the contribution from alingering echo. This echo will clearly be a filtered version of someearlier part of the signal x(t). In other words contributions to s(t)must come from earlier samples of x(t) than the ones contained in thevector [x(t−Q+1), . . . x(t)] which was discussed in the foregoinganalysis. These earlier samples will be convolved with further filtertaps corresponding to obstacles or effects which are outside the tapsthat are held in the vector h. These further taps are represented by thefilter g(t) so that:

s(t)=Σ_(k=Q) ^(M) g(k−Q)x(t−k)  Eq (8)

Where M is the maximum reverberation time (or lingering time of anecho). Moving to matrix notation, equations Eq (7) and Eq (8) can bewritten in block form as:

y(t)=X(t)h+X ₂(t)g+n(t)  Eq (9)

Here, X₂(t) contains the part of the signals in the matrix formdescribed above outside the central Q-tap-window, g is a vector with the“out-of-window” filter taps, and n(t) is a vector containing theadditive channel noise.

Using the inversion technique in accordance with the invention andleft-multiplying Eq (9) above with the pseudo-inverse matrix X(t)⁺gives:

X(t)⁺ y(t)=X(t)⁺ X(t)h+X(t)⁺ X ₂(t)g+X(t)⁺ n(t)

=ĥ(t)+X(t)⁺ X ₂(t)g+X(t)⁺ n(t)  Eq (10)

ĥ(t)=ĥ(t)+m(t)

Where m(t)=X(t)⁺X₂(t)g+X(t)⁺n(t)

For the first term on the right hand side of the second line, ifX(t)⁺X(t)=1 then ĥ(t)=h, and if not, then it is a least squaresminimum-norm estimate of h (according to the definition of theMoore-Penrose inverse matrix). The second term on this line relates tothe lingering echo. Although in general the lingering echo X₂(t)g willbe in the form of “structured” noise, the problem with which wasexplained in the introduction, X(t)⁺X₂(t)g will not have this structure.This is because the signals of x(t) contained in X(t) and X₂(t) are notdependent, and pre-multiplying the lingering echo X₂(t)g with X(t)⁺ hasthe effect of distorting the temporal structure in the lingering echo.Moreover, it will do so differently for different sliding matrix pairsX(t) and X₂(t). Hence, the lingering echo will not be a troublesome,repeating factor building up over time, but rather acts like an ordinarynoise component, similar to the component n(t). Put another way, thelingering echoes are a general source of noise, but by pre-multiplyingthis noise, structured or not, by a time-varying random-element basedmatrix, i.e. the inverse matrix X(t)⁺ any repetitive temporal structureis removed from the noise source. The consequence of this is that whenthe estimate ĥ(t), which might be identical with or close to the true h,is observed and added to a time-varying error term m(t), the effect ofsuch a noise term can be minimized by averaging ĥ(t)over a period oftime. A similar argument holds if ridge regression, is used as in Eqs(5).

It is stated hereinabove that methods and apparatus employing theinvention are advantageous over using cross-correlation. This will beexplained below, beginning with an explanation of how thecross-correlation referred to herein works.

Assuming a signal x(t) has been transmitted through a loudspeaker, it isreceived through a microphone again as y(t). The received signal y(t) istaken to relate to the transmitted one x(t) as follows:

$\begin{matrix}{{y(l)} = {{\left\lbrack {{x(t)}*{h(t)}} \right\rbrack (l)} = {\sum\limits_{k = 0}^{K - 1}\; {{x\left( {l - k} \right)}{h(k)}}}}} & {{Eq}\mspace{14mu} (A)}\end{matrix}$

That is, a sample of y(t) is a linear combination of the K last samplesof x(t) where the linear weights are given in the “filter coefficients”h(0), . . . h(K−1). To estimate the channel, it is necessary to estimatethese filter coefficients. In this technique the assumption is made thatthe signal x(t) is, for all t from—infinity to plus infinity, “white”.In other words it is assumed that the signal is uncorrelated with itselffor all non-zero shifts. Expressing this in an equation:

$\begin{matrix}\begin{matrix}{{\left\lbrack {{x(t)}*{x\left( {- t} \right)}} \right\rbrack (l)} = {\sum\limits_{k = {- \infty}}^{\infty}\; {{x\left( {l - k} \right)}{x\left( {- k} \right)}}}} \\{= {\sum\limits_{k = {- \infty}}^{\infty}\; {{x\left( {l + k} \right)}{x(k)}}}} \\{= \left\{ \begin{matrix}P & {{{if}\mspace{14mu} l} = 0} \\0 & {otherwise}\end{matrix} \right.}\end{matrix} & {{Eq}\mspace{14mu} (B)}\end{matrix}$

where P is a real, positive number.

Convolving a signal with its own time-reverse is the same as correlationwith the signal itself, i.e. computing the auto-correlation of thesignal. So assuming that x(t) is indeed white, correlating x(t) withitself yields a positive value P for a time lag of 0 and zero everywhereelse. Another way of writing this is:

x(t)*X(−t)=P·∂(t)  Eq (C)

where ∂(t) is the Dirac delta function.

It is now assumed that Eq (B) also holds approximately around a point t₀in time, with a length of N+1 samples and a time-window of x(t), sothat:

$\begin{matrix}\begin{matrix}{{\left\lbrack {{x(t)}*{x\left( {- t} \right)}} \right\rbrack_{t_{0}}(l)} = {\sum\limits_{k = {{- N}/2}}^{N/2}\; {{x\left( {l + t_{0} - k} \right)}{x\left( {t_{0} - k} \right)}}}} \\{= {\sum\limits_{k = {{- N}/2}}^{N/2}\; {{x\left( {l + k + t_{0}} \right)}{x\left( {k + t_{0}} \right)}}}} \\{\approx \left\{ \begin{matrix}P & {{{if}\mspace{14mu} l} = 0} \\0 & {otherwise}\end{matrix} \right.}\end{matrix} & {{Eq}\mspace{14mu} (D)}\end{matrix}$

Convolving y(t) with x(−t) around t₀ gives:

$\begin{matrix}{{\left\lbrack {{y(t)}*{x\left( {- t} \right)}} \right\rbrack_{t_{0}}(l)} = {\sum\limits_{k = {{- N}/2}}^{N/2}\; {{y\left( {l + t_{0} - k} \right)}{x\left( {t_{0} - k} \right)}}}} & {{Eq}\mspace{14mu} (E)}\end{matrix}$

and calculating the inner term in the sum gives:

$\begin{matrix}{{y\left( {l + t_{0} - k} \right)} = {\sum\limits_{i = 0}^{K - 1}\; {{x\left( {l + t_{0} - k - i} \right)}{h(i)}}}} & {{Eq}\mspace{14mu} (F)}\end{matrix}$

and hence:

$\begin{matrix}\begin{matrix}{{\left\lbrack {{y(t)}*{x\left( {- t} \right)}} \right\rbrack_{t_{0}}(l)} = {\sum\limits_{k = {{- N}/2}}^{N/2}{\sum\limits_{i = 0}^{K - 1}{{x\left( {l + t_{0} - k - i} \right)}{h(i)}{x\left( {t_{0} - k} \right)}}}}} \\{= {\sum\limits_{i = 0}^{K - 1}{\left\lbrack {\sum\limits_{k = {{- N}/2}}^{N/2}{{x\left( {l + t_{0} - k - i} \right)}{x\left( {t_{0} - k} \right)}}} \right\rbrack {h(i)}}}} \\{\approx {P \cdot {h(l)}}}\end{matrix} & {{Eq}\mspace{14mu} (G)}\end{matrix}$

As can be seen from Eq (D) above, the term in the brackets in Eq (G) isP (approximately) if and only if 1=i and (approximately) 0 otherwise.Hence by choosing l=1, the outcome of the convolution [y(t)*x(−t)]_(t) ₀(l) will be P times h(1), if l=2, then it is P times h(2) etc. In thisway, the filter coefficients h(.) can be estimated by convolving windowsof x(t) with windows of l(t) around a certain “centre” time sample t₀.However, the approximations in Eq (D) are just approximations. Inpractice, it is very hard to find sequences x(t) which have the propertyof being continuously locally white, and for many desirable choices of Nand K in the equations above, it is impossible. This limits theperformance of the overall estimation quality.

The cross-correlation method set out above could be put into a similarframework to the one used to demonstrate the invention in Eq (5 and 6).In this case, the estimator would be

ĥ _(Corr) =X(t)^(T) y

This is a poor estimator. In the case where X(t) is an orthogonalmatrix, the solution would be identical with the LS or LMS or ridgeestimators (up to a scaling constant). However, it is generallydifficult to construct a time-series x(t) with the property that therelated sliding window matrices X(t) are generally orthogonal. This isdue to the problem of finding sequences of relatively short length whichtake the form of orthogonal codes. As a consequence, code sequenceselection is mandatory in cross-correlation based methods, but are ofmuch less importance in the continuous LMS method.

In FIG. 6 an example of a sequence of impulse response estimates isshown. This example is an artificial case where the impulse-responsedoes not change but it seeks to show the relative resolution clarifies.Vertical slices of the picture correspond to impulse response estimatesat successive points in time, i.e. the pictures are position diagrams asset out above. In the left panel, the results obtained using across-correlation method are shown; and in the right panel, a continuousLMS-method in accordance with the invention has been used. The presenceof unwanted artefacts in the cross-correlation picture is apparent, andwill make subsequent tracking tasks harder than with LMS.

As previously set out in at least some preferred embodiments thebandwidth is deliberately limited beyond the constraints of the hardwareused. This has advantages in reducing the amount of computation requiredas will now be demonstrated.

In the case when signals are band-limited, there is temporal redundancyin the signals x(t), and hence, the rows and columns of the matricesX(t) will also exhibit redundancy. The effect of this is that the rankof the matrices X(t) is reduced. Although this will give reducedprecision in the estimation of the true impulse response h, it will alsoenable further computational reductions.

Let:

$\begin{matrix}{{X(t)} = {{USV}^{T} = {\sum\limits_{i = 1}^{{rank}{({X{(t)}})}}\; {\sigma_{i}u_{i}v_{i}^{T}}}}} & {{Eq}\mspace{14mu} (11)}\end{matrix}$

be an economised singular value decomposition of X(t), where (σ_(i)) arethe non-zero singular values, and {u_(i), v_(i)} are the left and righthand side singular vectors. The matrices U=(u₁, u₂, . . . , u_(r))V=(v₁, v₂, . . . , v_(r)) and S=diag(σ₁, σ₂, . . . , σ_(r)) contain ther=rank(X(t)) left and right singular vectors and the singular valuesrespectively. If X(t) is highly collinear, many of the singular valueswill be close to zero. This allows the approximation:

$\begin{matrix}{{{X(t)} \approx {{\overset{\sim}{X}}_{k}(t)}} = {{U_{k}S_{k}V_{k}^{T}} = {\sum\limits_{i = 1}^{k}\; {\sigma_{i}u_{i}v_{i}^{T}}}}} & {{Eq}\mspace{14mu} (12)}\end{matrix}$

Which is the k-component approximation to X(t). The matrices U_(k),S_(k), V_(k) contain only the k first singular vectors and values ofthis matrix. Furthermore, the pseudo-inverse matrix estimate of X(t) cannow be written as:

$\begin{matrix}{{{X(t)}^{+} \approx {{\overset{\sim}{X}}_{k}(t)}^{+}} = {{V_{k}S_{k}^{- 1}U_{k}^{T}} = {\sum\limits_{i = 1}^{k}\; {\sigma^{- 1}i\; v_{i}u_{i}^{T}}}}} & {{Eq}\mspace{14mu} (13)}\end{matrix}$

Since the matrices U_(k), S_(k), V_(k) now have a reduced number ofcolumns and rows, T and P are written as:

T=V _(k) S _(k) ⁻¹

P=U _(k) ^(T)

If X(t) is an (N×Q) matrix, then X(t)⁺ will be (Q×N), and hence T is Q×Kand P is (N×K). To apply the pseudo-inverse matrix of X(t) forestimating a channel h;

ĥ=X(t)⁺ y(t)≈{tilde over (X)} _(k)(t)y(t)=TPy(t)=T(Py(t))

Now, computing the product Py(t) requires N×K operation(multiply-and-sum operations), and computing the product of the resultPy(t) with T requires Q times K operations, which means that the totalnumber of operations is (N+Q)×K. The original method alternative wouldbe to multiply directly with X(t)⁺ which requires N×Q operations. In thecase where N=200, Q=70, and K=Q/3˜23 gives a sufficiently goodreduced-rank estimate of X(t) (typical when using a signal limited to ⅓of its original frequency band), the total number of operationsmultiplying y(t) with X(t)⁺ is 200×70=14,000, where as multiplying y(t)first with P and then with T gives a total of (N+Q)×K=(200+70)*23=6210operations, which means that more than half of the operational time hasbeen saved. The implementation of the proposed reduction involvesstoring two sets of matrices T,P for each time-window rather than one(X(t)⁺).

The foregoing gives rise to further preferred features of the invention.At least in some preferred implementations of the method of theinvention, the calculated inverse matrix comprises a pair of matricesderived from a subset of a singular value decomposition of the impulsesignal matrix, the method comprising the step of multiplying saidmatrices by a vector containing samples of said received signal.Preferably the method further comprises selecting a subset of samples ofsaid received signal to produce a subset vector and multiplying saidpair of matrices by said subset vector.

In accordance with another preferred feature the method comprisescalculating a subset of rows and/or columns of said inverse matrix andinterpolating between said subset to complete the calculated inversematrix.

An example of an implementation of the invention will now be describedmerely for ease of understanding and should not be understood aslimiting the scope of the invention.

A finger-tip tracking system was constructed using a standard PCloudspeaker driven by a sound card. Two standard PC microphones werealso connected to the sound card to record the reflected signal. Ofcourse dedicated ultrasound transducers or hydrophones could have beenused instead.

A digital white noise signal in the band 0 to 20 kHz where each temporalsample is drawn from the interval [−1,1] was generated by a digitalsignal processor (DSP) and passed through a digital-to-analogueconverter to the sound card and thus to the loudspeaker. The signalsreceived from the microphones were routed via the sound card to ananalogue to digital converter (i.e. a sampler). A sampling rate ofapproximately 22 kHz was used. The sampled signal was passed to the DSP.The DSP is also connected to a memory store holding the pre-calculatedpseudo-inverse matrix of the transmission signal. This is then used topre-multiply the matrices of received response samples in a window.

Typical parameters are as follows. Choosing the dimensions of the matrixX(t) to be 200×70, (estimating 70 filter taps using 200 snapshots of thex(t)-signals) and having 100 updates of the impulse responses estimatespr. second yields a total number of 200×70×100=1.4 millionmultiplication-and-sum operations per second, or 1.4 Megaflops. This isa modest number of operations which could be implemented in software asa background operation, or on an inexpensive DSP. The number ofoperations could be compressed to being integer operations. Furthermore,processor parallelism and single instruction multiple data instructionsand a designated co-processor can be used for this purpose.

The resultant calculated taps are plotted on a position diagram—similarto that shown in the right panel of FIG. 6. This is effectively a plotof displacement from the transmitter/receiver (vertical axis) againsttime (horizontal axis). The plots in FIG. 6 therefore relate to astationary reflector. A position diagram is plotted for each signal—i.e.for each loudspeaker.

In order to plot a two dimensional trace, the two diagrams arecontinuously combined using ellipse intersection to allow the outline ofmovement of the reflector to be plotted. FIG. 7 shows an example of apicture used to trace a finger around and the resulting plot obtainedfrom the apparatus described above. It will be observed that very smallmovements in the reflector are clearly resolved. For example it may beseen that the plot on the right hand side is actually made up of severalconsecutive tracings of the outline on the left hand side.

1. A human-machine interface system adapted to track movement of anobject in air wherein said human-machine interface comprises: anacoustic transmitter; an acoustic receiver; and logic configured toapply a calculated inverse matrix of an impulse signal transmitted bythe acoustic transmitter to a signal received by the acoustic receiver,wherein movement of the object is used to control the machine.
 2. Thesystem as claimed in claim 1 wherein said transmitter is configured totransmit said impulse signal at regular intervals.
 3. The system asclaimed in claim 1 wherein said transmitter is configured to transmitsaid impulse signal continuously.
 4. The system as claimed in claim 1comprising logic for calculating said inverse matrix.
 5. The system asclaimed in claim 1, wherein the calculated inverse matrix comprises aridge inverse matrix.
 6. The system as claimed in claim 1, wherein thecalculated inverse matrix comprises a Moore-Penrose inverse matrix. 7.The system as claimed in claim 1, further comprising logic configured tosample the received signal using a sliding window.
 8. The system asclaimed in claim 1, further comprising logic configured to generate theimpulse signal from a non-repeating code.
 9. The system as claimed inclaim 1, further comprising logic configured to generate the impulsesignal from a code having at least some respective portions linearlyindependent of one another.
 10. The system as claimed in claim 1,wherein the impulse signal comprises substantially white noise.
 11. Thesystem as claimed in claim 1, further comprising logic configured togenerate the impulse signal with a bandwidth which is a fraction of thebandwidth of the transmitter and receiver subsystem.
 12. The system asclaimed in claim 1, further comprising logic configured to select asubset of rows and/or columns of the inverse matrix to calculate. 13.The system as claimed in claim 12 comprising logic configured tointerpolate between the subset of rows and/or columns in the calculatedinverse matrix.
 14. The system as claimed in claim 1, further comprisinglogic configured to decompose the calculated inverse matrix into a pairof matrices derived from a subset of a singular value decomposition ofthe impulse signal matrix.
 15. The system as claimed in claim 14,further comprising logic configured to select a subset of samples of thereceived signal to produce a subset vector and multiplying the pair ofmatrices by the subset vector.
 16. The system as claimed in claim 1,further comprising logic configured to construct a position diagrambased on an estimated channel response resulting from applying thecalculated inverse matrix of the impulse signal to the return signal.17. The system as claimed in claim 1, further comprising a plurality oftransmitters configured to be driven by respective signals from the samefrequency band; the receiver being configured to separate the respectivereceived signals.
 18. The system as claimed in claim 1, furthercomprising logic configured to combine a plurality of estimated channelresponses using ellipse or ellipsoid intersection.
 19. The system asclaimed in claim 1, further comprising logic configured to sample theimpulse signal and the received response signal at the same rate.
 20. Acomputer readable medium comprising instructions, which, when executed,cause a computer to perform the method of claim
 1. 21. A computerreadable medium comprising instructions, which, when executed, cause acomputer to operate a human-machine interface which tracks movement ofan object in air wherein said human-machine interface comprises: anacoustic transmitter; an acoustic receiver; said instructions includinglogic configured to apply a calculated inverse matrix of an impulsesignal transmitted by the acoustic transmitter to a signal received bythe acoustic receiver, wherein movement of the object is used to controlthe machine.